Job Automation

Bubble Chart

Photo by Taiki Ishikawa on Unsplash

Robots will harvest, cook, and serve our food.
They will work in our factories, drive our cars, and walk our dogs.
Like it or not, the age of work is coming to an end…
— Gray Scott

Ingest data

country, city, line, start_year, end_year, time_comp, length, cost, etc.

# file path parameter
df_file_path <- "archetypes/job-automation/job-automation.csv"
df <- read.csv(df_file_path, header = TRUE, stringsAsFactors = FALSE)
df

Wrangle data

select all the visual variables, order by number employed (size variable)

df_wrangle <- df %>% select(prob, Average_annual_wage, education, short_occupation, numbEmployed)

df_wrangle <- df_wrangle[order(df_wrangle$numbEmployed, decreasing = TRUE),]

df_wrangle

Plot

x, y, size, color

prob
Average_annual_wage
education
short_occupation
numbEmployed

theme_opts <- theme(
    text = element_text(family = "inconsolata"), 
    plot.title = element_text(color = "black", size = 14, face = "bold"),
    plot.subtitle = element_text(color = "black", size = 12),
    plot.caption = element_text(color = "#555555", size = 8),
    # axis.title.x = element_blank(),
    # axis.title.y = element_blank(),
    # axis.text.x = element_text(vjust = 12),
    panel.border = element_blank(),
    panel.background = element_blank(),
    panel.grid.minor = element_blank(), # remove minor gridlines
    # panel.grid.major.x = element_blank(), # remove x (vertical) gridlines
    # panel.grid.major.y = element_blank(), # remove y (horizontal) gridlines
    legend.title = element_blank(), # remove legend title
    # legend.text = element_text(color = "black", size = 8),
    legend.position='none'
)

education_palette <- c(
  "High school diploma or equivalent" = "#d49a5e",
  "Bachelor's degree" = "#61a46c",
  "Postsecondary nondegree award" = "#f1d517",
  "Associate's degree" = "#61a46c",
  "No formal educational credential" = "#c6553c",
  "Doctoral or professional degree" = "#5080a2"
)

v1 <- ggplot(data = df_wrangle, aes(x = prob, y = Average_annual_wage)) +
    geom_point(aes(size = numbEmployed, fill = education), 
                           shape = 21, color = "#FFFFFF", stroke = 1.0, alpha = 0.7) +
    scale_x_continuous() +
    scale_y_continuous(breaks = seq(0, 300000, 25000), labels = scales::dollar) +
    scale_fill_manual(values = education_palette) +
    # scale_size_area(max_size = 10) +
    scale_size(range = c(2, 15)) +
    labs(title = "Job Automation",
        subtitle = "color by education level, sized by number employed",
        x = 'less likely <- probability of automation -> most likely',
        y = 'average annual wage',
        caption = "Source:Transit Costs Project")+
    theme_minimal() +
    theme_opts


girafe(ggobj = v1, width_svg = 16, height_svg = 12,
       options = list(opts_sizing(rescale = TRUE, width = 0.8)))

Wrangle

create a label subset

label_list <- c(
  "Orthodonists",
  "Dentists",
  "Airline Pilots, Copilots and Flight Engineers",
  "Podiatrists",
  "Preschool Teachers",
  "Recreation Workers",
  "Animal Control Workers",
  "Ambulance Drivers and Attendants",
  "Radiation Therapists",
  "Judges",
  "Home Health Aides",
  "Shoe and Leather Workers and Repairers",
  "Financial Advisors",
  "Geoscientists",
  "Cooks, Fast Food",
  "Food Preparation & Serving Workers",
  "Nuclear Technicians",
  "Benefits Managers",
  "Insurance Underwriters",
  "Sewers, Hand"
)

df_labels <- filter(df_wrangle, short_occupation %in% label_list)
df_labels

Plot

adding labels

v2 <- v1 +
  geom_text_repel(data=df_labels, aes(label = short_occupation), box.padding = 0.75, direction = "y", min.segment.length = 0.0, nudge_y = 0.75, point.padding = 0.5, family = "inconsolata")

girafe(ggobj = v2, width_svg = 16, height_svg = 12,
       options = list(opts_sizing(rescale = TRUE, width = 0.8)))

References

citations for narrative and data sources

Narrative sources: Bloomberg, GO
Data sources: Frey & Osborne, Bureau of Labor Statistics